docstr: Generates samples.
  
  Parameters:
      n_samples - number of samples to generate
      log_p_function - a function that returns log density for a specific sample
      burn_in_steps - number of burn-in steps for sampling
  
  Returns a tuple of two lists: (samples, log_p_function values for samples)
docstr_tok: ["Generates","samples",".","Parameters",":","n_samples","-","number","of","samples","to","generate","log_p_function","-","a","function","that","returns","log","density","for","a","specific","sample","burn_in_steps","-","number","of","burn-in","steps","for","sampling","Returns","a","tuple","of","two","lists",":","(","samples",",","log_p_function","values","for","samples",")"]
code: def get_samples(self, n_samples, log_p_function, burn_in_steps=50):
      """
      Generates samples.
  
      Parameters:
          n_samples - number of samples to generate
          log_p_function - a function that returns log density for a specific sample
          burn_in_steps - number of burn-in steps for sampling
  
      Returns a tuple of two lists: (samples, log_p_function values for samples)
      """
  
      raise NotImplementedError
  
file_path: GPyOpt/GPyOpt/util/mcmc_sampler.py
imports: import emcee
  from ..experiment_design import initial_design
label_desc: Use image node_id 2 for calling the McmcSampler obj's underlying member method code with example usage: obj.get_samples(n_samples, log_p_function, burn_in_steps) without return types